Early in [1]}, the acoustic speech was mapped to articulatory movement with the codebook. However, the results of their inversion were highly dependent on the quality of the codebook. Then, with the publishing of corpora containing parallel acoustic and articulatory data, data-driven inversion frameworks based on machine learning were proposed. And the Mel-scale frequency cepstral coefficients (MFCC) of the speech signals were first accepted as inputs and then mapped to articulatory movements. Later, other methods like hidden markov model [2]}, mixture density network [3]}, and deep belief network [4]} were proposed. Recently, with the rise of deep neural networks (DNNs), the deep bidirectional long short-term memory (DBLSTM) was used by [5]} in the AAI. Since then, most of AAI related works (e.g., [6]}, [7]}, [8]}, [9]}) used DBLSTM to deal with various applications but the inputs of the models were only audio speech features. As for the speaker-independent AAI, the vocal tract length normalization [7]} was proposed to transform the acoustic spaces of different speakers to a target one. [8]} proposed the idea of pre-train and fine-tune to improve the generalization performance on their own dataset. Especially, [12]} used one-dimensional convolution of different sizes to extract the audio feature. It improved the performance in speaker-independent case by adding extra phoneme information, achieving the state-of-the-art (SOTA) result on the public Haskins Production Rate Comparison (HPRC) [13]} dataset.
